Symmetric Market Integration of Wheat in the World
Bibliographic record
Abstract
This study determined market integration of wheat in the world using price time series (1966-2018) of major world producing countries. The data were sourced from FAO database and data analysis were performed using unit root tests, Engel-Granger and Johansen co-integration tests, Granger causality and impulse response tests, restricted vector auto-regression (VAR), and auto-regression integration moving average (ARIMA) models. The empirical evidence showed that the law of one price (LOP) or parity in prices failed to hold in these markets due to poor co- integration among these markets. Furthermore, the wheat prices of Indian, USA and China markets were efficient as they established long-run equilibrium. However, Australian, Canadian and France markets were observed to be autarkic markets as short-run disequilibrium adjustment processes will not lead to stable long-run prices. It was established that USA market prices is a relative follower and plays little or no role in the global wheat trade. Therefore, the study recommends that a network of wheat commodity network across the globe at almost equal distance from each other for the enhancement of market integration and price transmission should be designed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".